Evaluation of regression models and Bayes-Ensemble Regressor technique for non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) is referred to as the task of decomposing the aggregated power load of a residential or commercial building into appliance-level consumption without the installation of dedicated smart meters. NILM, mostly considered as a supervised learning problem, is crucial for energy monitoring and management as this approach can detect load malfunction and prevent wastage of energy consumption. For most of regression based NILM research, not much work has been done in analyzing data against temporal parameters such as day of the week, week of the month, week of the year, and various quarters in a year. Additionally, various NILM research works focus on finding the best regression model for predicting appliance consumption but discards the fact that appliance usage varies on factors such as seasons, different working hours, and weekends. This paper aims to evaluate some regression algorithms used towards NILM research based on 8 different training and testing Methods which according to our knowledge covered major factors that affect the appliance usage. The dataset used for the evaluation of the regression models, were collected from a research lab at Grenoble INP, in Grenoble, France. The evaluation results show that instead of one algorithm, individual regression algorithms generated favorable outcomes for various appliances based on different train-test Methods. Furthermore, a novel Bayesian optimized Ensemble regressor model for predicting individual appliance consumption from aggregated load data is also proposed. Instead of just using the aggregated power information, the proposed model also uses temporal information of the dataset to estimate accurate consumption output of individual appliances. Extensive simulation results corroborate the merits of the proposed approach, which outperforms the benchmarking Methods. To ensure reproducibility of the results by the research community and a potential future improvement of the framework by other researchers, the complete source code is provided in the following repository: https://github.com/Mohammad-Kaosain-Akbar/NILM-Ensemble-Bayesian-Optimization